TW202223785A - Computerized system and computer-implemented method for item correlation - Google Patents

Abstract

The present disclosure provides a computerized method item correlation including: receiving an indication of an order comprising at least one item; determining if the order is urgent based on an amount of time remaining until the items must ship to a customer; determining a location of each of a plurality of pickers; iteratively, for items in the order: identifying a picker closest to the item, the picker having a current job priority; correlating the closest picker and the item in a data structure; re-correlating, in the data structure, at least one item previously correlated with the closest picker to an alternate picker in response to the current job priority not being urgent; sending, to a user device of the closest picker, a location and item identifier associated with the item; and storing, in the data structure, a completion flag in correlation with the item upon receipt of an item-complete message.

Description

Object calibration and optimization system and computerized method

The present disclosure generally relates to computerized systems and methods for computer determination of object association and prioritization. In particular, embodiments of the present disclosure relate to inventive and non-conventional systems and methods for ensuring efficient association of items with pickers, allowing faster assignment, faster shipping to customers, and reducing shipping costs.

Order fulfillment is the complex endeavor of a business to deliver tangible goods to customers. This complexity grows substantially for businesses that supply a wide variety of goods, process large volumes of orders, or store items across large physical areas that include a single large warehouse, multiple warehouses, or even multiple small facilities spread over geographically dispersed areas. This complexity greatly increases order fulfillment costs because businesses must compensate employees for the time it takes to pick and prepare items for delivery. Additionally, as the time required for item pickers to transfer to items increases, businesses may not be able to fulfill as many orders and may lose customers to competitors offering similar products with shorter delivery times, thus reducing sales. In addition, businesses can be forced to hire additional employees, thereby increasing costs.

Additionally, customers are increasingly beginning to expect expedited shipping, in some cases requiring delivery on the same day or even the same hour as the order is placed. Fulfillment centers often rely on sophisticated computerized algorithms to identify a picker's ideal drop within a storage area or across a geographic area. These algorithms attempt to reduce the amount of time required to collect items in preparation for packaging and shipping. While this solution may be adequate for small-scale operations in fulfillment centers with low inventory, low order rates, predictable orders, and few stock keeping units (SKUs), this solution is impractical for larger operations actual. For example, the computational time required to analyze the many combinations of pickers and items and optimize their routes introduces inefficiencies in the picking operation that offset the desired time savings.

In addition, a large component of consumer costs is shipping costs. However, shipping costs are highly variable based on how the fulfillment center packs the item. Packaging costs can also vary significantly depending on box, tape, and other material choices. Shipping and packaging options further introduce combinations that maximize cost efficiency. But optimizing all these combinations needs to be handled before the item is assigned to the picker. As this processing time increases, it forces the picker to wait for assignments, which reduces picking efficiency. Thus, assignment algorithms save time for pickers after disseminating assignments, but traditional algorithms increase the delay between assignments with a net damage to order fulfillment efficiency.

Accordingly, there is a need for improved methods and systems of computational algorithms to efficiently analyze various combinations of packaging, picker delivery, and picker assignments to achieve optimal picker assignment without delaying the specified time. With these systems and methods, picking operation efficiency can be increased while delivery times are reduced, thereby reducing overall business costs and increasing customer satisfaction.

One aspect of the present disclosure is directed to a computerized system for object association, comprising: at least one processor; and at least one non-transitory storage medium, including, when executed by the at least one processor, causing the at least one processor to execute Instructions comprising the steps of: receiving an indication of an order including at least one item; determining whether the order is urgent based on the amount of time remaining before the item must be shipped to the customer; determining the location of each of a plurality of pickers ; for the items in the order, iteratively do the following: identify the picker closest to the item, the picker has the current work priority; associate the closest picker with the item in the data structure; in the data structure, respond Reassociate at least one item previously associated with the nearest picker with a replacement picker if the current work priority is not urgent; send the location and item identifier associated with the item to the user of the nearest picker a device; and after receiving an object completion message from the user device indicating that the object has been picked, storing a completion flag associated with the object in the data structure.

Another aspect of the present disclosure is directed to a computer-implemented method for item association, comprising: receiving an indication of an order including at least one item; determining whether the order is urgent based on an amount of time remaining before the item must be shipped to a customer ; determine the location of each of the multiple pickers; for the items in the order, iteratively do the following: identify the picker closest to the item, the picker has the current job priority; in the data structure Associate the closest picker with the item; in the data structure, re-associate at least one item previously associated with the closest picker with the alternate picker in response to the current work priority not being urgent; will associate with the item The location and item identifier of the item are sent to the nearest picker's user device; and after receiving an item completion message from the user device indicating that the item has been picked, the completion flag associated with the item is stored in the data structure.

Yet another aspect of the present disclosure is directed to a computer-implemented method for item association, comprising: receiving an indication of an order including at least one item; prioritizing the order based on an amount of time remaining before the item must be shipped to a customer level; determine the location of each of the plurality of pickers; for the pending items of the order, iteratively do the following: access at least one data structure to determine the location of the pending item; determine within a threshold distance to the location set of nearby pickers; access at least one data structure to determine previous items associated with each of the set of nearby pickers; remove from the set the number of previous items with a number above a queue threshold Any nearby pickers; pickers within the extended threshold distance are added to the set in response to the set being empty after the picker's removal; pickers from nearby in response to priority being urgent The set of pickers identifies the picker closest to the item; the picker with the smallest queue distance is identified from the set of nearby pickers in response to the priority being Normal, where the queue distance represents the picker's closest distance to the picker how far to approach the associated item; associate the pending item with the identified picker in at least one data structure; send the location associated with the item and the item identifier to the nearest picker's user device; and After receiving the message that the object has been picked, the user device stores the completion flag associated with the object in the data structure.

Other systems, methods, and computer-readable media are also discussed herein.

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. Although several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, components and steps shown in the figures may be replaced, added, or modified, and the description described herein may be modified by substituting, reordering, removing steps, or adding steps to the disclosed methods Illustrative method. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Rather, the proper scope of the invention is defined by the appended claims.

Embodiments of the present disclosure are directed to systems and methods configured for efficient grouping of ordered items into packages.

Referring to FIG. 1A, depicted is a schematic block diagram 100 illustrating an exemplary embodiment of a system including a computerized system for enabling communications for shipping, transportation, and logistics operations. As shown in FIG. 1A, system 100 may include various systems, each of which may be connected to each other via one or more networks. The systems may also be connected to each other via direct connections (eg, using cables). The depicted system includes shipping authority technology (SAT) system 101, external front end system 103, internal front end system 105, shipping system 107, mobile device 107A, mobile device 107B, and mobile device 107C, seller portal 109, shipping and Order tracking (shipment and order tracking; SOT) system 111, fulfillment optimization (fulfillment optimization; FO) system 113, fulfillment messaging gateway (FMG) 115, supply chain management (supply chain management; SCM) system 117, warehouse management system 119, mobile device 119A, mobile device 119B, and mobile device 119C (depicted as being located inside fulfillment center (FC) 200), 3rd party fulfillment system 121A, 3rd party fulfillment system 121B, and 3rd party fulfillment system 121A 3-party fulfillment system 121C, fulfillment center authorization (FC Auth) 123 , and labor management system (LMS) 125 .

In some embodiments, the SAT system 101 may be implemented as a computer system that monitors order status and delivery status. For example, the SAT system 101 can determine whether an order is past its Promised Delivery Date (PDD) and can take appropriate action, including placing a new order, re-shipping items in an undelivered order, canceling an undelivered order, initiating and ordering Customer's contact, or the like. The SAT system 101 may also monitor other data, including outputs (such as the number of packages shipped during a particular time period) and inputs (such as the number of empty cartons received for shipment). SAT system 101 may also act as a gateway between different devices in system 100, enabling communication between devices such as external front end system 103 and FO system 113 (eg, using store-and-forward or other techniques).

In some embodiments, the external front-end system 103 may be implemented as a computer system that enables an external user to interact with one or more of the systems 100 . For example, in an embodiment where the system 100 enables the presentation of the system to allow a user to place an order for an item, the external front end system 103 may be implemented as a web server that receives retrieval requests, renders item pages, and requests payment information. For example, the external front end system 103 may be implemented as a computer or computer running software, such as an Apache HTTP server, Microsoft Internet Information Service (IIS), NGINX, or the like. In other embodiments, the external front-end system 103 may operate designed to receive and process requests from external devices (eg, mobile device 102A or computer 102B), obtain information from databases and other data repositories based on their requests, and Customized web server software that provides responses to received requests based on the information obtained.

In some embodiments, the external front end system 103 may include one or more of a web cache system, a database, a retrieval system, or a payment system. In one aspect, external front-end system 103 may include one or more of these systems, while in another aspect, external front-end system 103 may include an interface to one or more of these systems (eg, , server-to-server, database-to-database, or other network connection).

Some operations of the external front-end system 103 will be facilitated by the illustrative set of steps shown in FIGS. 1B , 1C, 1D, and 1E. External front-end system 103 may receive information from systems or devices in system 100 for presentation and/or display. For example, the external front-end system 103 may host or provide one or more web pages, including a search results page (SRP) (eg, FIG. 1B ), a Single Detail Page (SDP) (eg, FIG. 1C ), A shopping cart page (eg, Figure 1D), or an order page (eg, Figure 1E). A user device (eg, using mobile device 102A or computer 102B) can navigate to external front-end system 103 and request a retrieval by entering information into a retrieval box. External front-end system 103 may request information from one or more of systems 100 . For example, the external front end system 103 may request information from the FO system 113 to satisfy the retrieval request. The external front end system 103 may also request and receive (from the FO system 113) a Promised Delivery Date or "PDD" for each product included in the search results. In some embodiments, the PDD may indicate when a package containing the product will arrive at the user's desired location or promise to deliver the product to the user if ordered within a certain period of time (eg, by the end of the day (11:59 PM)) An estimate of the date at the user's desired location. (PDD is discussed further below with respect to the FO system 113.)

The external front-end system 103 may prepare the SRP based on the information (eg, FIG. 1B ). The SRP may contain information to satisfy the retrieval request. For example, this may include images of products that satisfy the retrieval request. The SRP may also contain individual prices for each product, or information related to enhanced delivery options, PDDs, weights, sizes, quotes, discounts, or the like for each product. The external front end system 103 may send the SRP (eg, via a network) to the requesting user device.

The user device may then select a product from the SRP, eg, by clicking or tapping the user interface or using another input device, to select the product represented on the SRP. The user device may formulate a request for information about the selected product and send it to the external front end system 103 . In response, the external front end system 103 may request information related to the selected product. For example, the information may include additional information in addition to the information presented for the products on the respective SRP. This may include, for example, shelf life, country of origin, weight, size, number of items in the package, disposal instructions, or other information about the product. Information may also include recommendations of similar products (based on, for example, vast amounts of data and/or machine learning analysis of customers who purchased this product and at least one other product), answers to frequently asked questions, reviews from customers, manufacturer information, image, or similar.

The external front-end system 103 may prepare an SDP (Single Detail Page) based on the received product information (eg, Figure 1C). The SDP may also contain other interactive elements, such as a "buy now" button, an "add to cart" button, a quantity bar, an image of the item, or the like. The SDP may further contain a list of sellers offering the product. The listing can be sorted based on the price offered by each seller so that the seller offering the product at the lowest price can be listed at the top. The listing may also be sorted based on seller rankings such that the highest ranked sellers may be listed at the top. The seller ranking may be developed based on a number of factors including, for example, the seller's past track record of meeting the PDD of commitments. The external front end system 103 may deliver (eg, via a network) the SDP to the requesting user device.

The requesting user device may receive an SDP listing product information. After receiving the SDP, the user device may then interact with the SDP. For example, a user of the requesting user device may click or otherwise interact with a "put in cart" button on the SDP. This adds the product to the shopping cart associated with the user. The user device may transmit this request to add a product to the shopping cart to the external front end system 103 .

The external front end system 103 may generate a shopping cart page (eg, Figure ID). In some embodiments, the shopping cart page lists products that the user has added to a virtual "shopping cart." A user device may request a shopping cart page by clicking on or otherwise interacting with an icon on an SRP, SDP, or other page. In some embodiments, the shopping cart page may list all the products that the user has added to the shopping cart, along with information about the products in the shopping cart (such as the quantity of each product, the price per item per product, each product price based on the associated quantity), information about the PDD, delivery method, shipping cost, user interface elements for modifying products in the shopping cart (for example, deleting or modifying quantities), for ordering additional products or setting up products , an option to set up interest payments, a user interface element to advance to a purchase, or the like. A user at the user device may click on or otherwise interact with a user interface element (eg, a button that says "Buy Now") to initiate a purchase of the products in the shopping cart. Buy. After doing so, the user device may transmit this request to initiate a purchase to the external front end system 103 .

External front end system 103 may generate an order page (eg, FIG. 1E ) in response to receiving a request to initiate a purchase. In some embodiments, the order page relists items from the shopping cart and requests the entry of payment and shipping information. For example, an order page may include requesting information about the purchaser of the items in the shopping cart (eg, name, address, email address, phone number), information about the recipient (eg, name, address, phone number, delivery information), shipping information (e.g., speed/method of delivery and/or picking), part of payment information (e.g., credit card, bank transfer, check, stored points), requesting a cash receipt (e.g., for tax purposes) ) user interface element, or similar. The external front end system 103 may send the order page to the user device.

The user device may enter information about the order page and click or otherwise interact with user interface elements that send the information to the external front end system 103 . From here, the external front-end system 103 can send information to various systems in the system 100 to enable new orders to be created and processed with the products in the shopping cart.

In some embodiments, the external front-end system 103 may be further configured to enable sellers to transmit and receive order-related information.

In some embodiments, internal front end system 105 may be implemented as a computer system that enables internal users (eg, employees of an organization that owns, operates, or leases system 100 ) to interact with one or more of systems 100 . For example, in an embodiment where the system 100 enables the presentation of the system to allow a user to place an order for an item, the internal front end system 105 may be implemented to enable the internal user to view diagnostic and statistical information about the order, modify item information, or review Web server for order-related statistics. For example, the internal front end system 105 may be implemented as a computer or computer running software such as an Apache HTTP server, Microsoft Internet Information Services (IIS), NGINX, or the like. In other embodiments, the internal front-end system 105 may operate designed to receive and process requests from the systems or devices depicted in the system 100 (as well as other devices not depicted), from databases and other data stores based on their requests The library obtains the information, and based on the obtained information serves custom web server software that provides responses to received requests.

In some embodiments, the internal front end system 105 may include one or more of a web cache system, a database, a retrieval system, a payment system, an analysis system, an order monitoring system, or the like. In one aspect, internal front end system 105 may include one or more of these systems, while in another aspect, internal front end system 105 may include an interface to one or more of these systems (eg, , server-to-server, database-to-database, or other network connection).

In some embodiments, the transportation system 107 may be implemented as a computer system that enables communication between the systems or devices in the system 100 and the mobile devices 107A-107C. In some embodiments, the transportation system 107 may receive information from one or more mobile devices 107A to 107C (eg, mobile phones, smart phones, PDAs, or the like). For example, in some embodiments, mobile devices 107A-107C may comprise devices operated by delivery workers. Delivery workers, who may be permanent, temporary, or shift employees, may utilize mobile devices 107A-107C to effect delivery of packages containing products ordered by the user. For example, to deliver a package, a delivery worker may receive a notification on a mobile device indicating which package to deliver and where to deliver the package. Upon arrival at the delivery location, the delivery worker may locate the package (eg, in the back of a truck or in the package's crate), scan or otherwise capture an identifier (eg, a barcode) on the package with a mobile device , images, text strings, RFID tags, or the like) and deliver the package (for example, by leaving it at the front door, leaving it to a guard, giving it to the recipient, or the like) ). In some embodiments, the delivery worker may use the mobile device to capture a photo of the package and/or obtain a signature. The mobile device may send information to the transportation system 107 including information about the delivery, including, for example, time, date, GPS location, photo, an identifier associated with the delivery worker, an identifier associated with the mobile device, or similar. Transportation system 107 may store this information in a database (not depicted) for access by other systems in system 100 . In some embodiments, the shipping system 107 may use this information to prepare and send tracking data to other systems, indicating the location of a particular package.

In some embodiments, some users may use one type of mobile device (eg, permanent workers may use specialized PDAs with customized hardware such as barcode scanners, styluses, and other devices), while others use Alternatively, other types of mobile devices may be used (eg, off-the-shelf mobile phones and/or smart phones may be utilized by temporary workers or shift workers).

In some embodiments, the transportation system 107 may associate a user with each device. For example, the transportation system 107 may store users (represented by, for example, user identifiers, employee identifiers, or telephone numbers) and mobile devices (represented by, for example, International Mobile Equipment Identity (IMEI), International Mobile Equipment Identifier (International Mobile Subscription Identifier; IMSI), phone number, Universal Unique Identifier (UUID) or Globally Unique Identifier (Globally Unique Identifier; GUID) representation). The transportation system 107 may use this association in conjunction with data received at the time of delivery to analyze the data stored in the database to determine, among other things, the location of the worker, the efficiency of the worker, or the speed of the worker.

In some embodiments, seller portal 109 may be implemented as a computer system that enables sellers or other external entities to communicate electronically with one or more of systems 100 . For example, a seller may utilize a computer system (not depicted) to upload or provide product information, order information, contact information, or the like for products the seller wishes to sell via system 100 using seller portal 109 .

In some embodiments, shipping and order tracking system 111 may be implemented as a computerized system that receives, stores, and forwards information about the location of packages containing products ordered by customers (eg, by users using device 102A to device 102B). . In some embodiments, the shipping and order tracking system 111 may request or store information from a web server (not depicted) operated by the shipping company that delivers the package containing the product ordered by the customer.

In some embodiments, shipping and order tracking system 111 may request and store information from systems depicted in system 100 . For example, shipping and order tracking system 111 may request information from shipping system 107 . As discussed above, the transportation system 107 may be from one or more mobile devices 107A to mobile devices 107C (eg, a delivery truck) associated with one or more of a user (eg, a delivery worker) or a vehicle (eg, a delivery truck). , mobile phone, smart phone, PDA or similar) to receive information. In some embodiments, shipping and order tracking system 111 may also request information from warehouse management system (WMS) 119 to determine the location of individual products within a fulfillment center (eg, fulfillment center 200 ). Shipping and order tracking system 111 may request data from one or more of shipping system 107 or WMS 119, process the data upon request, and present the data to devices (eg, user device 102A and user device 102B).

In some embodiments, fulfillment optimization (FO) system 113 may be implemented as a computerized system that stores information on customer orders from other systems (eg, external front-end system 103 and/or shipping and order tracking system 111 ). The FO system 113 may also store information describing where particular objects are kept or stored. For example, some items may be stored in only one fulfillment center, while some other items may be stored in multiple fulfillment centers. In still other embodiments, certain fulfillment centers may be designed to store only certain sets of items (eg, freshly produced or frozen products). The FO system 113 stores this information and associated information (eg, quantity, size, date of receipt, date of expiration, etc.).

The FO system 113 may also calculate the corresponding PDD (Promised Delivery Date) for each product. In some embodiments, the PDD may be based on one or more factors. For example, the FO system 113 may calculate a PDD for a product based on past demand for the product (eg, how many times the product has been ordered over a period of time), expected demand for the product (eg, forecast for upcoming how many customers will order the product during the upcoming period), network-wide past demand indicating how many products have been ordered during the period, network-wide expected demand indicating how many products are expected to be ordered during the upcoming period, One or more counts of products stored in each fulfillment center 200, which fulfillment center stores each product, expected or current orders for the product, or the like.

In some embodiments, the FO system 113 may periodically (eg, hourly) determine the PDD for each product and store it in a database for retrieval or sending to other systems (eg, external front-end systems 103, SAT systems) 101. Shipping and order tracking system 111). In other embodiments, FO system 113 may receive electronic requests from one or more systems (eg, external front end system 103, SAT system 101, shipping and order tracking system 111) and compute PDDs on demand.

In some embodiments, fulfillment communication gateway (FMG) 115 may be implemented to receive a request or response in one format or agreement from one or more systems in system 100 (such as FO system 113 ), convert it to another a format or protocol and forward it in the converted format or protocol to other systems (such as WMS 119 or 3rd party fulfillment system 121A, 3rd party fulfillment system 121B or 3rd party fulfillment system 121C) and vice versa system.

In some embodiments, the supply chain management (SCM) system 117 may be implemented as a computerized system that performs forecasting functions. For example, the SCM system 117 may forecast the level of demand for a particular product based on, for example, past demand for the product, expected demand for the product, network-wide past demand, network-wide expected demand, storage on each Counted products in a fulfillment center 200, expected or current orders for each product, or the like. In response to this forecast level and the volume of each product in all fulfillment centers, the SCM system 117 may generate one or more purchase orders to purchase and reserve sufficient quantities to meet forecast demand for a particular product.

In some embodiments, the warehouse management system (WMS) 119 may be implemented as a computer system that monitors workflow. For example, WMS 119 may receive event data indicative of discrete events from individual devices (eg, device 107A-device 107C or device 119A-device 119C). For example, WMS 119 may receive event data indicating that a package is scanned using one of these devices. As discussed below with respect to fulfillment center 200 and FIG. 2, during the fulfillment process, machines (eg, automated or hand-held barcode scanners, RFID readers, high-speed cameras, such as tablet computers 119A) may be utilized at certain stages of the fulfillment process. , mobile device/PDA 119B, computer 119C device, or the like) to scan or read package identifiers (eg, barcode or RFID tag data). The WMS 119 may store each event indicating a scan or reading of the package identifier along with the package identifier, time, date, location, user identifier, or other information in a corresponding database (not depicted), and may The information is provided to other systems (eg, shipping and order tracking system 111).

In some embodiments, WMS 119 may store data that associates one or more devices (eg, devices 107A to 107C or devices 119A to 119C) with one or more users (the one or more users and the system 100 ). associated) related information. For example, in some cases a user (such as a part-time employee or a full-time employee) may be associated with a mobile device because the user owns the mobile device (eg, the mobile device is a smart phone). In other cases, the user may be associated with the mobile device because the user temporarily holds the mobile device (eg, the user gets the mobile device at the beginning of the day, will use the mobile device during the day, and will Return the mobile device at the end of the day).

In some embodiments, WMS 119 may maintain a work log for each user associated with system 100 . For example, the WMS 119 may store information associated with each employee, including any specified process (eg, unloading from trucks, picking items from pick areas, rebin wall work, packing items), use of person identifier, location (eg, floor or zone in fulfillment center 200), number of units moved through the system by employees (eg, number of items picked, number of items packed), and device (eg, device 119A to 119C) associated identifiers, or the like. In some embodiments, WMS 119 may receive registration and logout information from a timing system, such as a timing system operating on devices 119A-119C.

In some embodiments, 3rd party fulfillment (3PL) systems 121A through 121C represent computer systems associated with third party providers of logistics and products. For example, while some products are stored in fulfillment center 200 (as discussed below with respect to FIG. 2 ), other products may be stored off-site, may be produced on demand, or may not be otherwise available for storage in fulfillment center 200 . 3PL systems 121A-3PL systems 121C may be configured to receive orders from FO system 113 (eg, via FMG 115 ) and may provide products and/or services (eg, delivery or installation) directly to customers. In some embodiments, one or more of 3PL system 121A-3PL system 121C may be part of system 100, while in other embodiments, one or more of 3PL system 121A-3PL system 121C may be part of system 100 External (eg, owned or operated by a third-party provider).

In some embodiments, the fulfillment center Auth system (FC Auth) 123 may be implemented as a computer system with various functions. For example, in some embodiments, FC Auth 123 may serve as a single-sign on (SSO) service for one or more other systems in system 100 . For example, FC Auth 123 may enable the user to log in via the internal front end system 105, determine that the user has similar privileges to access resources at the shipping and order tracking system 111, and enable the user to log in without the need for a second login process obtain their privileges. In other embodiments, FC Auth 123 may enable a user (eg, an employee) to associate himself/herself with a particular task. For example, some employees may not have electronic devices (such as devices 119A-119C) and may actually move from task to task and from zone to zone within fulfillment center 200 during the course of the day. FC Auth 123 can be configured to enable their employees to indicate what tasks they are working on and where they are located at different times of the day.

In some embodiments, labor management system (LMS) 125 may be implemented as a computer system that stores attendance and overtime information for employees, including full-time and part-time employees. For example, LMS 125 may receive information from FC Auth 123, WMS 119, device 119A-device 119C, transportation system 107, and/or device 107A-device 107C.

The particular configuration depicted in Figure 1A is merely an example. For example, although FIG. 1A depicts FC Auth system 123 connected to FO system 113, not all embodiments require this particular configuration. Indeed, in some embodiments, the systems in system 100 may be connected to each other via one or more public or private networks, including the Internet, an intranet, a Wide-Area Network ; WAN), Metropolitan-Area Network (MAN), IEEE 802.11a/b/g/n compliant wireless network, leased line, or the like. In some embodiments, one or more of the systems in system 100 may be implemented as one or more virtual servers implemented at a data center, server farm, or the like.

FIG. 2 depicts fulfillment center 200 . Fulfillment center 200 is an example of a physical location that stores items for shipping to customers at the time of ordering. Fulfillment center (FC) 200 may be divided into multiple zones, each of which is depicted in FIG. 2 . In some embodiments, these "zones" can be thought of as virtual divisions between the different stages of the process of receiving, storing, retrieving, and transporting items. Thus, although "zones" are depicted in Figure 2, other zone divisions are possible, and in some embodiments zones in Figure 2 may be omitted, duplicated, or modified.

Inbound area 203 represents the area of FC 200 that receives items from sellers who wish to sell products using system 100 from FIG. 1A. For example, a seller may use truck 201 to deliver item 202A and item 202B. Item 202A may represent a single item large enough to occupy its own shipping pallet, while item 202B may represent a collection of items stacked together on the same pallet to save space.

Workers will receive the item in the inbound area 203 and can use a computer system (not depicted) to check the item for damage and correctness as appropriate. For example, a worker may use a computer system to compare the quantity of items 202A and 202B with the ordered quantity of items. If the quantities do not match, the worker may reject one or more of item 202A or item 202B. If the quantities do match, the worker can move those items to the buffer zone 205 (using, for example, a trolley, walker, forklift, or manually). Buffer 205 may be a temporary storage area for items that do not currently need to be in the pick area (eg, due to the presence of a high enough number of items in the pick area to meet forecast demand). In some embodiments, forklift 206 operates to move items around buffer zone 205 and between inbound area 203 and unload area 207 . If item 202A or item 202B in the pick area is required (eg, due to forecast demand), a forklift may move item 202A or item 202B to unload area 207 .

The unloading area 207 may be an area of the FC 200 where items are stored prior to being moved to the picking area 209 . Workers assigned to picking tasks ("pickers") can approach items 202A and 202B in the picking area, use a mobile device (eg, device 119B) to scan the barcodes in the picking area, and scan with item 202A and the barcode associated with item 202B. The picker may then take the item to the picking area 209 (eg, by placing the item on a cart or carrying the item).

Picking area 209 may be an area of FC 200 where items 208 are stored on storage unit 210 . In some embodiments, storage unit 210 may include one or more of physical shelves, bookshelves, boxes, totes, refrigerators, freezers, cold storage areas, or the like. In some embodiments, the picking area 209 may be organized into multiple floors. In some embodiments, workers or machines can move items into the picking area 209 in a variety of ways, including, for example, forklifts, elevators, conveyors, carts, walkers, carts, automated robots or devices, or manually . For example, a picker may place items 202A and 202B on a trolley or cart in unloading area 207 and walk items 202A and 202B to picking area 209 .

Pickers may receive instructions to place (or "stow") items at specific points in pick area 209 , such as specific spaces on storage unit 210 . For example, a picker may scan item 202A using a mobile device (eg, device 119B). The device may indicate to the picker where the item 202A should be stowed, eg, using a system of indicating aisles, racks, and locations. The device may then prompt the picker to scan the barcode at the location prior to stowage of the item 202A at that location. The device may send data (eg, via a wireless network) to a computer system such as WMS 119 in FIG. 1A , indicating that item 202A has been stowed at that location by a user using device 119B.

Once the user places an order, the picker may receive instructions on device 119B to retrieve one or more items 208 from storage unit 210 . The picker may retrieve the item 208 , scan the barcode on the item 208 , and place the item 208 on the transport mechanism 214 . Although the transport mechanism 214 is shown as a slide, in some embodiments, the transport mechanism may be implemented as one or more of a conveyor belt, elevator, cart, forklift, walker, dolly, or the like. Object 208 may then arrive at packing area 211 .

The packing area 211 may be the area of the FC 200 that receives items from the picking area 209 and packs the items into boxes or bags for eventual shipping to customers. In the packing area 211, the worker assigned to receive the item ("confluent worker") will receive the item 208 from the picking area 209 and determine which order the item 208 corresponds to. For example, a confluence worker may use a device such as computer 119C to scan the barcode on item 208. Computer 119C can visually indicate to which order item 208 is associated. This may include, for example, spaces or "cells" on the wall 216 that correspond to orders. Once the order is complete (eg, because the cell contains all the items for that order), the confluence worker can instruct the packer (or "packer") that the order is complete. A packer can retrieve items from the cells and place the items in boxes or bags for shipping. The packer may then send the box or package to the hub zone 213, eg, via a forklift, cart, dolly, cart, conveyor, manually or otherwise.

The hub area 213 may be the area of the FC 200 that receives all boxes or packets (“packages”) from the packaging area 211 . Workers and/or machines in the hub zone 213 may retrieve the packages 218 and determine which portion of the delivery area each package is expected to go to, and deliver the packages to the appropriate camp zone 215 . For example, if the delivery area has two smaller sub-areas, the package will go to one of the two camp areas 215. In some embodiments, a worker or machine may scan the package (eg, using one of devices 119A-119C) to determine its final destination. Delivering the package to the camp area 215 may include, for example (eg, based on a zip code) determining a portion of the geographic area to which the package is destined, and determining the camp area 215 associated with the portion of the geographic area.

In some embodiments, camp area 215 may include one or more buildings, one or more physical spaces, or one or more areas in which packages are received from hub area 213 for sorting to routes and/or sub-routes middle. In some embodiments, camp area 215 is physically separate from FC 200 , while in other embodiments, camp area 215 may form part of FC 200 .

Workers and/or machines in camp area 215 may determine which route and/or sub-route the package 220 should be associated with, for example, based on a comparison of the destination to existing routes and/or sub-routes, a comparison of each Calculations of workloads for the route and/or sub-routes, timing, shipping method, cost of shipping the package 220, PDDs associated with items in the package 220, or the like. In some embodiments, a worker or machine may scan the package (eg, using one of devices 119A-119C) to determine its final destination. Once a package 220 is assigned to a particular route and/or sub-route, workers and/or machines can move the package 220 to be shipped. In exemplary FIG. 2, camp area 215 includes trucks 222, cars 226, and delivery workers 224A and 224B. In some embodiments, truck 222 may be driven by delivery worker 224A, which is a full-time employee delivering packages for FC 200 , and truck 222 is owned, leased, or operated by the same company that owns, leases, or operates FC 200 . In some embodiments, car 226 may be driven by delivery worker 224B, which is a "flexible" or temporary worker delivering on an as-needed basis (eg, seasonally). Car 226 may be owned, rented or operated by delivery worker 224B.

3 illustrates an exemplary embodiment of a method for object association in a data structure consistent with disclosed embodiments. FIG. 3 illustrates the steps of process 300 . FO system 113 may perform process 300 to generate a data structure for managing picking operations. In step 302, the FO system 113 begins by receiving an indication of an order that includes at least one item. Orders may constitute various items and quantities distributed in a warehouse, multiple warehouses, or other storage locations. In some cases, an item may include a quantity of bulk goods that requires a picker to visit multiple locations to obtain the requested quantity. For example, an order may call for 1,000 pounds of corn, but each of the multiple storage locations contains only 300 pounds of corn. Similarly, an item may require multiple separately stored pieces, such as a bicycle with a frame and tires stored in different locations in a warehouse. Thus, process 300 may facilitate specifying by a picker items that require multiple stops, as well as individual items that require a single stop. Process 300 may also assign multiple pickers to items requiring multiple stops, thereby distributing picking operations and speeding up package preparation.

At step 304, the FO system 113 may determine whether the order is urgent based on the amount of time remaining before the item must be shipped to the customer. Step 304 may include calculation of the PDD based on the order, and determining the expected time required for shipping. Thus, the FO system 113 can determine how long the fulfillment center must process, locate, pack, and send the package for shipping. If this amount of time is less than the threshold, the FO system 113 may classify the order and each of the items of the order as urgent. For example, the FO system 113 may determine that the order is urgent if the fulfillment center has less than 30 minutes before the order must leave the center and go into shipping to reach the customer with the PDD. Alternatively, if the time is greater than 30 minutes, the FO system 113 may determine that the order is of normal priority.

Additionally, customers can assign urgency priority to orders at the time of purchase, such as by opting to pay an additional fee for faster delivery. Businesses can also assign urgency priorities based on customer priorities such as Tier 1 program membership, item unstorability, picker availability, special disposal requirements for items that include weight and size restrictions or special equipment needs, shipping regulations , transportation, tolls, and other shipping cost considerations.

At step 306, the FO system 113 determines the location of each of the plurality of pickers. For example, a picker's user device, such as device 119B, may report the device location to FO system 113 . Each picker on the warehouse floor can carry a separate unit. A device may include hardware and/or software to determine the orientation of a location. The devices may determine their respective locations and report to the FO system 113, such as using WiFi or cellular signals to report the location of the device as determined by a GPS receiver in the device. In some embodiments, a device may determine location by measuring signal strength (such as WiFi) and triangulating the device location based on multiple WiFi signals. Alternatively or additionally, the FO system 113 may determine the location of the device. For example, warehouses may also have other sensors, such as IR sensors, which receive IR signals from user devices that transmit identification codes; or RFID sensors, which register RFID tags placed on user devices. exist. Warehouses may also contain cameras to visually identify and locate pickers and/or their associated devices, carts, and packages. Visual recognition can be aided by recognizing images such as QR codes, face recognition, and the like. The FO system 113 can associate these codes in the database with devices and corresponding users.

At step 308, the FO system 113 begins iteratively associating items with pickers in the data structure for the items in the order. In some embodiments, the FO system 113 may associate each item in the order. Alternatively, the FO system 113 may associate only a subset of orders first, reserving a second subset for a later time. For example, an order may contain both urgent and normal priority items. The FO system 113 may associate urgent items from an order, as well as urgent items from other orders, before associating normal priority items.

In step 308, the FO system 113 identifies the picker closest to the item, the picker having the current work priority. Step 308 may further include determining from a data repository where the object is located in a warehouse, geographic area, and the like. That is, the data repository may contain a list of object identifiers and corresponding locations, and the FO system 113 may query the data repository to retrieve location identifiers, such as geographic coordinates, warehouse numbers, floor numbers, bay numbers, shelf numbers, and the like By. In some embodiments, the distance may be a direct line between the item and the picker, ignoring any intervening obstacles. Alternatively, each of the plurality of distances may include the total length of the path between the picker location and the item location in order to avoid obstacles. The FO system 113 may determine a route by, for example, determining the line between the origin and the destination, and for points along the line, identifying the closest points available for travel, such as spaces between shelves, roads, and the like, And redetermines the line between the closest point and the destination until all points of the route are on the traversable path. Distance can also be measured as the amount of time it takes to travel between two points, rather than the geometric length of the path. That is, the FO system 113 may employ an algorithm to determine the shortest route and expected travel time between two points when traveling around any obstacle such as shelves, pillars, walls, or doors as reflected in the warehouse's storage map. The FO system 113 may also employ algorithms that take into account the distance between floors, such as in multi-storey warehouses. In some embodiments, the FO system 113 may determine the shortest paths along highways and surface streets, as well as distances for parking, walking, or other modes of transportation.

When the FO system 113 identifies the closest picker, the FO system 113 may also determine the picker's current work priority. A picker performing a picking operation may have a current work priority that reflects the priority of his currently assigned item. For example, picker A may locate an item assigned an urgent priority because it must be shipped immediately to reach the customer at the promised time, while picker B locates an item assigned a normal priority. In this case, Picker A has a current work priority of "Urgent" and Picker B has a current work priority of "Normal". The FO system 113 may determine the picker's current work priority by sending an inquiry to the picker's user device 119B to determine the current assignment. The FO system 113 may also query a data structure containing associations between picker identifiers, item identifiers, and priorities, as will be discussed with reference to step 310 .

At step 310, the FO system 113 associates the nearest picker with the item in the data structure. In some embodiments, the data structure may be a dictionary, JSON file, SQL database, or the like that contains item identifiers associated with picker identifiers. The data structure can contain additional information, such as association time, order identifiers, or item locations. The data structure may also contain associated sequences, such as the order of items assigned to pickers. For example, three item A, item B, and item C may be associated with picker Z. If the FO system 113 associates object A at 11:15, object B at 11:02, and object C at 11:50, the objects may have the order of B:1, A:2, C:3. Each item and its respective sequence can be associated with a picker identifier Z in the data structure. Furthermore, the order of objects can be based on distance, such that closer objects have less order. The picker's user device may display the items according to the individual item order, thereby indicating the order in which the picker should locate the item.

The order may also be based on a pick sequence optimized to reduce overall pick time. For example, in some embodiments, the FO system 113 may determine the shortest path connecting each of the items associated with the picker, and assign an order to the items based on the determination. To make this determination, the FO system 113 may begin by determining the picker's nearest item. The FO system 113 may then determine the closest objects to the previous closest objects, and iteratively add objects to the sequence until all objects have been added. The FO system 113 may also employ other algorithms, such as performing random selection of objects, and iteratively eliminating path intersections of the paths until no intersections remain.

At step 312, the FO system 113 may re-associate at least one item previously associated with the closest picker with the alternate picker in the data structure in response to the current work priority not being urgent. For example, if the picker's current job priority is normal, then the items previously associated with the picker also have normal priority. If the picker is the nearest picker for the urgent item, the FO system 113 can re-associate the normal item with the alternate picker so that the nearest picker can advance to the urgent item. In some embodiments, previously associated items may be disassociated from the most recent picker and kept in memory for later association. For example, the FO system 113 may associate all items of an urgent order before previously associated non-urgent items. In this way, urgent items can be associated before normal priority items so that pickers can quickly begin urgent operations.

In some embodiments, the FO system 113 may re-associate a subset of items previously associated with the nearest picker, thereby allowing some items to remain associated with the nearest picker. For example, as previously described, items associated with the most recent picker may be ordered in the data structure, such as by respective association times. In the sequence, the first N objects may be fixed, and the remaining M objects may be transformable. Furthermore, the length of the sequence can be limited to N+M items. Accordingly, re-associating at least one item previously associated with the most recent picker may include re-associating the transformable item. As an illustration, pickers may be limited to 10 associated items (N+M=10). Of those 10 items, item 1 to item 3 in the sequence may remain fixed (N=3), while item 4 to item 10 may be transformable (M=7). If the picker is identified as the closest picker for the urgent item, the FO system 113 may re-associate items 4 through 10 with the alternate picker, thereby associating items 1 through 3 with the closest picker. The FO system 113 may add urgent objects to the data structure (ie, set to 1) in the order of the beginning of the sequence. Alternatively, the FO system 113 may add urgent items to the data structure in the order of the end of the fixed segment (ie, put at 3). Such restrictions on object placement and redistribution can reduce the computational resources that would otherwise be necessary to re-associate all objects. Furthermore, this may allow for more efficient picking operations by allowing the picker to remain focused on the task rather than switching tasks. Furthermore, in some embodiments, the FO system 113 may select the second closest picker in response to the first closest picker having a sequence length of N+M. For example, the maximum sequence length may be 10 (N+M=10). If an item's closest picker already has 10 item associations in the data structure, the FO system 113 may associate the new item with the second closest picker to avoid exceeding the first picker's maximum sequence length.

At step 314, the FO system 113 sends the location associated with the item and the item identifier to the nearest picker's user device. In some embodiments, the FO system 113 may also send a route to the user device, such as a walking or driving route calculated when ordered based on location determinations, as described above. Additionally, the FO system 113 may provide images of items and/or locations to assist pickers.

At step 316, the FO system 113 receives an item complete message from the user device indicating that the item has been picked. For example, a picker may touch a button on the user interface of the user device to indicate that the picker has found the associated item. In some embodiments, the picker may scan the item's markings, such as barcodes, QR codes, RFID tags, and the like. This may further indicate that the item is being delivered for packaging and shipping. In some scenarios, pickers may place items into containers for transport to packing stations. When the item is placed in the container, the picker can indicate that the item is complete. At step 318, the FO system 113 stores the completion flag associated with the object in the data structure. Completion flag record objects no longer need to be associated with pickers in the data structure. If the item has a done flag, the FO system 113 may immediately or periodically disassociate the item from any pickers.

At step 320, the FO system 113 determines whether there are any remaining items in the order. If step 320 is yes, the FO system 113 returns to step 308 to continue associating the item with the picker. However, if all items of the order have been associated, step 320 is NO and the FO system 113 returns to step 302 to receive an indication of a new order. In some embodiments, the FO system 113 may continue to receive indications of new orders while associating items. The FO system 113 may hold the new order indication in a buffer until an item that can be used to associate the order is processed. The FO system 113 may also distribute processing to multiple processors with read and write access to data structures, allowing synchronization of associated activities.

4 is a flowchart showing an exemplary embodiment of optimizing object processing, consistent with disclosed embodiments. In some scenarios, a picker may instruct his container to fill up before the container is actually full. For example, pickers may do so to meet faster container quotas. If the picker is instructed to deliver the container to the packing station once the container is full, the picker can instruct his container to be full so he can travel directly to the packing station and avoid having to travel to distant parts of the warehouse to pick Associated objects. However, these types of actions greatly reduce picking efficiency and are not conducive to promoting delivery and customer satisfaction. Accordingly, the process 400 provides a method for the FO system 113 to track picker efficiency in a data structure to prioritize associations with successful pickers.

At step 402 of process 400, the FO system 113 receives a plurality of item completion messages from a user device of a picker among the plurality of pickers. For example, many different pickers may be active in the warehouse, and each of them may submit item completion messages. When the FO system 113 receives an object completion message, the FO system 113 may log the message by adding the completion flag associated with the object to the data structure.

At step 404, the FO system 113 receives a job completion message from the user device of the picker among the plurality of pickers. For example, a job completion message can indicate that the picker has filled the container, and the picker can initiate the message by selecting a button on the user interface of the user device. After receiving the job completion message, the FO system 113 may access the data structure and at step 406 count the number of items associated with the picker with the completion flag. The FO system 113 may also archive items marked as complete. In step 406, the FO system 113 may count the number of items that have a completion flag and are associated with the picker. Step 406 may include constructing a query to retrieve each entry having the picker's identifier. At step 408, the FO system 113 compares the number of items to a success threshold to determine failure. For example, a fulfillment center may have a success threshold of three items per container. If a picker's container remains less than three items but the picker marks the container as full, the fulfillment center may consider this container inefficient and count it as a failure to maximize efficiency. If the number of items associated with the picker with completion flags is greater than the success threshold, step 408 is NO, and the FO system 113 returns to step 402 to receive additional item completion messages and job completion messages. On the other hand, if the number is less than the success threshold, then step 408 is yes and the FO system 113 proceeds to step 410 to log the failure. For example, the FO system 113 may log the failure in a data structure associated with the picker. The FO system 113 may also log failures in a separate data structure with picker information such as employee ID, shift time, success rate, failure rate, and other efficiency metrics.

For example, if a particular item is large and the container cannot hold many items, the picker may not fully utilize the container due to need. However, if the picker frequently fails to fill the container, it may indicate that the picker is inefficient. Therefore, the FO system 113 compares the number of failures to the failure threshold at step 412 . For example, the failure threshold may be a number, such as three failures in total, or a ratio, such as three failures in an hour. As part of step 412, the FO system 113 may generate a query to find the number of failures associated with the picker. If the number of failures is less than the failure threshold, step 412 is NO, and the FO system 113 returns to step 402 . However, if the number of failures is greater than the failure threshold, then step 412 is YES and the FO system 113 at step 414 sends a signal to lock the picker's user device. Locking the user device may include, for example, preventing the user device from displaying additional location and object identifiers. In some embodiments, the user device may require manager code to unlock. In this way, an inefficient picker may need to access the manager and explain why the picker has a high failure rate. Additionally, the FO system 113 may record that the picker has been locked. The FO system 113 can then re-associate the items assigned to the locked picker so that the new picker can continue to locate the item. When associating items with pickers, the FO system 113 may also prioritize pickers that have not been locked. For example, because the first picker has a high failure rate, the FO system 113 may associate the item with a second picker that is further away from the item than the first picker.

In some embodiments, the threshold for success may be based on, for example, the fulfillment center's packaging requirements. Different fulfillment centers can have different sized containers. For example, urban fulfillment centers may have more small orders and thus smaller containers than rural fulfillment centers. Success thresholds can also be calculated based on item size, and the FO system 113 can compare the container capacity to the picker's combined volume and/or weight of the associated item. Thus, success thresholds can vary by pick location, so that pickers at one location have the same success threshold, or by item attributes, so that containers have different depending on which items are associated with the picker success threshold.

In some scenarios, an order may contain multiple items. Items may require delivery to a single address. In this case, even though it may take a long time for a picker to locate multiple items, it may be beneficial for a single picker to locate multiple items of an order to reduce packaging and shipping costs for multiple shipments . For example, if an order has two items, and the first item is positioned on the first floor of the warehouse and the second item is positioned on the second floor, the picker may require a significant amount of time to travel between the two items. However, the cost of this lost productivity may be less than the cost of deploying the two items into a single shipment at the packaging stage, or the cost of shipping the items independently.

Accordingly, the FO system 113 may employ the process 500 to optimize item associations based on shipping costs. 5A is a flowchart showing an illustrative embodiment of a method for pairwise object association, consistent with disclosed embodiments. At step 502, the FO system 113 determines the location of the second item of the order, and at step 504, determines the distance between the second item and the first item. This distance can be a radial distance, or can be based on delivery considerations.

At step 506, the FO system 113 calculates a first cost based on shipping the first item and the second item separately. This first cost may include the calculated value of the packaging material for the two packages, such as the two boxes and the tape and label for each box. The first cost may also include disposal charges for tracking individual packages in the package tracking system. At step 508, the FO system 113 calculates a second shipping cost based on shipping the first item and the second item together. The second shipping cost may thus include packaging materials and disposal charges for a single package. Both the first cost and the second cost may also include the price of shipping weight and taxes.

At step 510, the FO system 113 determines the difference between the first cost and the second cost, and at step 512 calculates an amount of time based on the difference. The amount of time may also be based on the picker cost, which in turn may be based on the picker's hourly wages, taxes, insurance, and other benefits, as well as any rest periods. For example, a picker can earn $14 per hour, and the employer also pays $2 in taxes and $3 in insurance and an average year-end bonus of $1 per hour for a year, for a total cost of $20 per hour for a picker. However, pickers can have a ten-minute break every hour, resulting in a 50-minute work time. Therefore, the picker cost can be $0.40 per minute. At the same time, it can cost $2 to ship two items of an order separately, and $1 to ship the same two items together. Thus, assigning two items to a single picker may be beneficial if the picker can travel to and locate the second item in less than 2.5 minutes. In some embodiments, the amount of time may be based on longer distances and road and/or air travel, and includes costs such as fuel, tolls, tickets, vehicle maintenance, and vehicle depreciation.

At step 514, the FO system 113 compares the distance between the first item and the second item calculated at step 514 to a threshold value. The FO system 113 may set the threshold value to be the distance traveled by the nearest picker during the amount of time. The threshold value may be based on the amount of time calculated at step 512 multiplied by the average picker speed. For example, the average picker can walk 300 feet per minute. Continuing the above example, if the second item is less than 750 feet away, the picker will be able to reach the item in less than 2.5 minutes, allowing two pickers to deliver the item at a lower overall cost than two pickers selecting the item and shipping the item separately object. In some embodiments, the FO system 113 may also calculate the cost of delivering the second picker to the second item, and add this cost to the time and distance threshold calculations at step 512 . Alternatively, the FO system 113 may omit steps 506-512 and use a standard threshold value for setting the distance, such as 100 feet. If step 514 is no, the FO system 113 proceeds to step 518 to associate the second item with a different picker than the picker associated with the first item. If step 514 is yes, then the FO system 113 proceeds to step 516 to compare the number of picker associations with the number of items currently associated with the picker. Step 516 may include querying the data structure to count the number of associations. If the number of associations is less than the sum of N fixed objects and M changeable objects (i.e., if the number is less than N+M), so that the picker's association will not exceed a total of N+ by adding one more object association M, then step 516 is YES and the FO system 113 proceeds to step 520 to associate the second item with the nearest picker. However, if the number of associations is greater than or equal to N+M, step 516 is NO and the FO system 113 proceeds to step 518 to associate the second item with a different picker than the first item.

Process 500 may be further understood with reference to Figures 5B and 5C, which illustrate diagrammatic representations of object pairings consistent with disclosed embodiments. FIG. 5B shows warehouse 522 with racks 524 . The figure further shows two pickers 528A and 530A with user device 528B and user device 530B. User device 528B and user device 530B depict picker associations. For example, user device 528B shows picker 528A associated with item number 2. FIG. 5B further shows three objects, object 1 a , object 1 b and object 2 . Item 1a and Item 1b are from the same order, while Item 2 is from a different order. The object 1a can be regarded as the first object, and the object 1b can be regarded as the second object. Additionally, Figure 5B shows a radius 526 around the object 1a. Object 1b is within radius 526 as depicted. Therefore, step 514 of process 500 is yes, and FO system 113 has associated both the first item (item 1 a ) and the second item (item 1 b ) with picker 530A at step 520 . This association is shown in user device 530B. However, FIG. 5C shows an alternative result of process 500 . While item lb is positioned within radius 526 in FIG. 5B , item lb of FIG. 5C falls outside radius 526 . Therefore, step 514 of process 500 is no. The FO system 113 therefore associates the second item (item 1b) with a different picker (picker 528A) than the first item (1a) at step 518 . Thus, item lb is associated with picker 528A, as reflected by user device 528B.

While the FO system 113 may receive many orders with urgent priority, some orders may have normal priority. Furthermore, in some scenarios, eg, during periods of high order rates, the re-association of items at step 312 of process 300 or the association of items from locked pickers at step 414 of process 400 may be delayed until sufficient to process Ability is available. These objects can reside in buffers. Alternatively, these items may be stored in a data structure that does not have picker associations. Furthermore, after a period of picker movement throughout the area, better delivery and association may become possible to improve efficiency relative to earlier solutions.

6A is a flowchart showing an exemplary embodiment of a method for optimizing object associations, consistent with disclosed embodiments. As shown in FIG. 6A, the FO system 113 may begin process 600 at step 602 by determining the number of items that do not have picker associations in the data structure. The FO system 113 may query buffers and/or data structures to identify items that require initial association, such as normal priority items from new orders, or items that require re-association, such as from pickers later assigned to urgent items object.

Allocating many items at once can consume resources and prevent the FO system 113 from processing urgent items. Thus, process 600 contains test conditions that allow FO system 113 to run process 600 when improved efficiency is desired. For example, at step 604, the FO system 113 determines the average ratio of items per picker. This ratio may be, for example, the overall item entries in the data structure divided by the overall active pickers, where items marked as complete have been removed from the data structure or otherwise not counted at step 604 .

At step 606 , the FO system 113 compares the number of items to a backlog threshold, and at step 608 compares the average ratio to a task assignment threshold. If any of the comparisons at steps 606 and 608 are negative, the FO system 113 proceeds to step 610 to wait a certain period of time before rechecking. However, if the comparisons at both steps 606 and 608 are yes, then the FO system 113 begins using step 612 to re-associate the item with the picker. In this manner, the FO system 113 avoids using processing resources if there are an insufficient number of item associations pending, or if the picker task assignment is insufficient. In either of these situations, the backlog of pending associations for objects can be resolved without computationally expensive optimization efforts. In some embodiments, however, such as when computational costs are low, process 600 may omit both or either of steps 606 or 608. Thus, the FO system 113 may perform the re-association step at regular time intervals, such as every minute.

At step 612, the FO system 113 calculates object densities for the plurality of regions. For example, the FO system 113 may divide the picking area into multiple areas, such as areas of equal size, or areas of equal number of items. The FO system 113 may query the data structure to determine the location of each of the pending objects, count the number of objects in each of the plurality of regions, and divide the number by the region. Similarly, at step 614, the FO system 113 calculates the picker density for the plurality of zones by determining the location of each of the active pickers. Additionally, the FO system 113 at step 616 calculates the participation rate of item density and picker density for each of the plurality of zones.

Steps 612 to 616 may be further understood with reference to Figures 6B and 6C, which illustrate graphical representations of picker reassignment, consistent with disclosed embodiments. Both Figures 6B and 6C illustrate a warehouse with four zones 602-608. Additionally, item 1 to item 8 and pickers are positioned throughout the warehouse. Turning to Figure 6B, in zone 602, there is one item (item 3) and two pickers, while zone 604 contains four items (1, 2, 6, and 7) and one picker. Thus, region 602 has an object density of 1 (one object per region), while region 604 has an object density of 4 (4 objects in that region). In a similar manner, zone 602 has a picker density of 2 and zone 604 has a picker density of 2. Thus, zone 602 has a participation rate of 0.5 (one item in the zone, 2 pickers in the zone), and zone 604 has a participation rate of 4 (four items in the zone, 1 picker in the above area).

Returning to Figure 6A, at step 618, the FO system 113 selects a reassignment picker from the first zone with the lowest participation rate. The selection may be random among the pickers in the first area, or the FO system 113 may identify the picker with the lowest number of item associations. At step 620, the FO system 113 re-associates the original item from the reassigned picker with the individual picker in the first area in the data structure. Additionally, at step 622, the FO system 113 re-associates in the data structure the reassigned items from the pickers in the second region with the highest participation rate. After the FO system 113 has performed the re-association, the FO system 113 sends the location and item identifier associated with the re-associated item to the user device of the re-assigned picker. In other words, through steps 618 to 622, the FO system 113 selects a picker to move to a busier area (ie, an area with a higher participation rate), and moves the picker's item to a less busy area (ie, an area with a higher participation rate) , a different picker in an area that also has a lower participation rate), and moves items from the busier picker in the high participation area to the reassigned picker. For example, after a certain period of time, a picker may locate many items in one area, but the FO system 113 may not move them to a new area because the picker is not within the radius to be considered close enough to newer items to assign to newer objects. This can leave underutilized pickers in empty areas while repeatedly tasking pickers in busy areas and even over-tasking pickers in busy areas. Through process 600, the FO system 113 may distribute task assignments more equally and improve picking efficiency.

Again, FIG. 6B helps illustrate process 600 . As previously mentioned, zone 602 has a participation rate of 0.5 and zone 604 has a participation rate of 4. Similarly, zone 606 has a participation rate of 1, and zone 608 has a participation rate of 2. Thus, zone 602 has the lowest participation rate, while zone 604 has the highest participation rate. Accordingly, the FO system 113 will select a re-assignment picker from the area 602 in step 618 of the process 600, transfer the picker's assignment to the remaining pickers, and provide the picker with the new item in the area 604. This is shown in Figure 6C, where the picker has moved from zone 602 to zone 604. Thus, the participation rate for zone 602 is now 1 (instead of 0.5, as in Figure 6B), and the participation rate for zone 604 is now 2 (instead of 4, as in Figure 6B).

7 is a flowchart showing an exemplary embodiment of a method for object association in a data structure, consistent with disclosed embodiments. As stated above, some orders may contain only normal priority items. Because the FO system 113 may prioritize urgent priority items, the FO system 113 may delay the association of normal priority items, or may perform the association in a different manner that prioritizes efficiency over speed.

At step 702 of the process 700, the FO system 113 determines whether the order is normal priority based on the amount of time remaining before the item must be shipped to the customer. For example, an order may have several days before it is required to ship. The FO system 113 then iteratively begins associating the item with the picker for the pending item in the order and in response to the order being normal priority.

At step 704, the FO system 113 identifies the location of the pending object, eg, by querying the data structure. Using the location, the FO system 113 at step 706 identifies a plurality of nearby pickers that are within a threshold distance to the location. For example, this distance can be a radius. Step 706 may allow the FO system 113 to pre-screen potential pickers and avoid unnecessary analysis of distant pickers. For example, FIGS. 7B and 7C are diagrammatic illustrations of optimized picker associations consistent with disclosed embodiments. Turning to Figure 7B, which depicts warehouse 522, three pickers 722A, pickers 724A, and pickers 726 are on the warehouse floor. Object 1 is also positioned on the ground, and a radius 720 is drawn around Object 1 . Pickers 726 are outside the radius and thus will be skipped during the remainder of the analysis provided by process 700 to improve processing efficiency.

After the FO system 113 has determined the nearby pickers, the FO system 113 begins iteratively determining the picker's current association and path for each of the nearby pickers. Accordingly, at step 708, the FO system 113 determines the location of the previously associated item of the nearby picker. The FO system 113 then sets the nearby picker's queue distance at step 710 to the distance between the nearby picker and the nearest previously associated item of the nearby picker. The queue distance thus represents the closest of all items currently associated with the picker. After the FO system 113 completes steps 708 and 710 for the first picker, the FO system 113 determines at step 712 whether there are remaining nearby pickers to analyze. If there are remaining pickers, step 712 is yes and the FO system 113 returns to step 708 to analyze additional pickers. If there are no remaining pickers, step 712 is NO and the FO system 113 proceeds to step 714 to associate the pending item with the nearby picker with the smallest queue distance. In some cases, there may be only a single nearby picker. If this occurs, the FO system 113 may omit steps 708-712. The FO system 113 then proceeds to step 716 and determines whether there are remaining pending items. If so, step 716 is yes and the FO system 113 returns to step 704 to identify the location of the pending object. Otherwise, step 716 is NO, and the FO system 113 proceeds to step 718 to wait for a new order. Alternatively, the FO system 113 may use the process 700 to correlate any uncorrelated items in the backlog or buffer rather than a single order. Process 700 may also provide an initial normal priority association for pickers who have exhausted urgent priority assignments and should therefore return to normal current work priority.

FIG. 7C shows step 708 and step 710 . In FIG. 7C, picker 722A and picker 724 are shown with respective associated user device 722B and user device 724B. For example, picker 722A is associated with item 2 and item 3. The FO system 113 then determines the locations of item 2, item 3, item 4, and item 5 in warehouse 522 at step 708, as shown. The FO system 113 also determines the distance from the picker to the picker's associated item. For example, picker 724A is associated with item 4 and item 5. FO system 113 identifies route 724C to object 4 and route 724D to object 5 . In this embodiment, the FO system 113 determines the distance from the route, but the FO system 113 may instead determine the distance as a direct line between two points. At step 710, the FO system 113 sets the picker's queue distance to the shortest distance between the picker and the existing designation. Thus, for picker 722A, the FO system 113 identifies the length of route 722D as the queue distance for picker 722A and the length of route 724C as the queue distance for picker 724A. Next, at step 714, the FO system 113 compares the distance of route 722D to the distance of route 724C and determines that route 722D is shorter. Therefore, the FO system 113 will associate the new item 1 with the picker 722A. Thus, the process 700 may enable the FO system 113 to aggregate close items and associate them with a picker, while allowing other pickers to quickly transfer remote items without unnecessary detours, thereby improving the picker efficiency.

In yet another embodiment, the FO system 113 may process a set of orders with mixed priorities. The FO system 113 may receive an indication of an order that includes at least one item; prioritize the order based on the amount of time remaining before the item must be shipped to the customer; and determine the location of each of the plurality of pickers. The FO system 113 may then iteratively perform the following operations for the pending items of the order: access at least one data structure to determine the location of the pending item; determine the set of nearby pickers within a location to a threshold distance; access at least one data structure structure to determine previous items associated with each of the sets of nearby pickers; and remove any nearby pickers from the set having a number of previous items above a queue threshold. The FO system 113 may also add pickers within the extended threshold distance to the set in response to the set being empty after the picker's removal. The FO system 113 may proceed by identifying the picker closest to the item from the set of nearby pickers in response to the priority being urgent; and identifying the picker from the set of nearby pickers in response to the priority being normal The picker with the smallest queue distance, where the queue distance represents how far the picker is from the picker's closest associated item. Having identified the picker with the smallest queue distance, the FO system 113 may then associate the pending item with the identified picker in at least one data structure; send the location associated with the item and the item identifier to The user device of the most recent picker; and after receiving a message from the user device that the item has been picked, storing a completion flag associated with the item in the data structure.

Although the present disclosure has been shown and described with reference to specific embodiments of the present disclosure, it should be understood that the present disclosure may be practiced in other environments without modification. The foregoing description has been presented for purposes of illustration. The foregoing description is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those of ordinary skill in the art from consideration of this specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, those of ordinary skill in the art will appreciate that aspects of the disclosed embodiments may also be stored on other types of computer-readable media, such as the following A secondary storage device such as a hard disk or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive media.

Computer programs based on written descriptions and disclosed methods are within the skill of experienced developers. Various programs or program modules may be created using any of the techniques known to those of ordinary skill in the art or may be designed in conjunction with existing software. For example, a program section or program module can be or by means of .Net Framework (.Net Framework), .Net Compact Framework (.Net Compact Framework) (and related languages such as Visual Basic, C, etc. ), Java, C++, Objective-C, HTML, HTML/AJAX combination, XML, or HTML with Java applets.

Furthermore, although illustrative embodiments have been described herein, those of ordinary skill in the art will recognize equivalent elements, modifications, omissions, combinations (eg, of aspects in various embodiments) based on this disclosure , the scope of any and all embodiments of adaptation and/or modification. The limitations in the scope of claims should be construed broadly based on the language employed in the scope of claims, and are not limited to examples described in this specification or during the prosecution of this application. Instances shall be considered non-exclusive. Additionally, the steps of the disclosed methods may include modification in any manner by reordering the steps and/or inserting or deleting steps. Therefore, it is intended that the specification and examples be regarded as illustrative only, with the true scope and spirit being indicated by the following claims and their full scope of equivalents.

100: System 101: Shipping Authorization Technical System 102A: Mobile Devices/User Devices 102B: Computer/User Device 103: External Front-End Systems 105: Internal Front-End Systems 107: Transportation Systems 107A, 107B, 107C: Mobile Devices 109: Seller Portal 111: Shipping and Order Tracking System 113: Execution optimization system 115: Fulfill the communication gateway 117: Supply Chain Management Systems 119: Warehouse Management System 119A: Mobile Devices/Tablets 119B: Mobile Device/PDA 119C: Mobile Devices/Computers 121A, 121B, 121C: 3rd Party Fulfillment Systems 123: Fulfillment Center Authorization System 125: Labor Management System 200: Fulfillment Center 201, 222: Truck 202A, 202B, 208: Objects 203: Inbound area 205: Buffer 206: Forklift 207: Unloading area 209: Picking area 210: Storage Unit 211: Packaging area 213: Hub Area 214: Transport Agency 215: Camp Area 216: Wall 218, 220: Package 224A, 224B: Delivery workers 226: Car 300, 400, 500, 600: Process 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 402, 404, 406, 408, 410, 412, 414, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 702, 704, 706, 708, 710, 712, 714, 716, 718: Steps 522: Warehouse 524: Shelves 526, 720: Radius 528A, 530A, 722A, 724A, 726: Pickers 528B, 530B, 722B, 724B: User Devices 722D, 724A, 724C, 724D: Routes 602, 604, 606, 608: District

1A is a schematic block diagram showing an exemplary embodiment of a network including a computerized system for enabling communications for shipping, transportation, and logistics operations, consistent with the disclosed embodiments. FIG. 1B depicts a sample Search Result Page (SRP) including one or more search results and interactive user interface elements that satisfy a search request, consistent with disclosed embodiments. 1C depicts a sample Single Detail Page (SDP) including a product and information about the product and interactive user interface elements, consistent with disclosed embodiments. 1D depicts a sample shopping cart page including items in a virtual shopping cart and interactive user interface elements, consistent with disclosed embodiments. 1E depicts a sample order page including items from a virtual shopping cart and information about purchase and shipping, and interactive user interface elements, consistent with disclosed embodiments. 2 is a diagrammatic illustration of an exemplary fulfillment center configured to utilize the disclosed computerized system, consistent with disclosed embodiments. 3 is a flowchart showing an exemplary embodiment of a method for object association in a data structure, consistent with disclosed embodiments. 4 is a flowchart showing an exemplary embodiment of optimizing object processing, consistent with disclosed embodiments. 5A is a flowchart showing an illustrative embodiment of a method for pairwise object association, consistent with disclosed embodiments. 5B and 5C are diagrammatic illustrations of pairings of objects consistent with disclosed embodiments. 6A is a flowchart showing an exemplary embodiment of a method for optimizing object associations, consistent with disclosed embodiments. 6B and 6C are diagrammatic illustrations of picker reassignment, consistent with disclosed embodiments. 7A is a flowchart showing an illustrative embodiment of a method for object association in a data structure, consistent with disclosed embodiments. 7B and 7C are diagrammatic illustrations of optimized picker associations consistent with disclosed embodiments.

300: Process

302, 304, 306, 308, 310, 312, 314, 316, 318, 320: Steps

Claims (20)

A computerized system for object association, comprising: at least one processor; and At least one non-transitory storage medium comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform steps comprising: receive instructions for an order that includes at least one item; determining whether the order is urgent based on the amount of time remaining before the item must be shipped to the customer; determining the location of each of the plurality of pickers; For the items in the order, iteratively do the following: Identify the picker closest to the item, the picker having the current work priority; associating the nearest picker with the item in the data structure; in the data structure, re-associating at least one item previously associated with the closest picker with an alternate picker in response to the current work priority not being urgent; sending a location and an item identifier associated with the item to the nearest picker's user device; and After receiving an object completion message from the user device indicating that the object has been picked, a completion flag associated with the object is stored in the data structure. The system of claim 1, wherein The items previously associated with the closest picker are ordered in the data structure according to the respective association time, the first N items of the sequence are fixed and the remaining M items are changeable, wherein the length of the sequence is limited to N+ M objects; and Re-associating at least one item previously associated with the closest picker includes re-associating a transformable item. The system of claim 2, wherein the steps further comprise: receiving a job completion message from a user device of a picker of the plurality of pickers; counting the number of items associated with the picker with the completion flag; comparing the number of objects to a success threshold to determine failure; and When the number of failures exceeds a failure threshold, a signal is sent to lock the user device of the picker. The system of claim 3, wherein the success threshold is based on packaging requirements. The system of claim 2, wherein identifying the picker closest to the item comprises: The second closest picker is selected in response to the first closest picker having a sequence length of N+M. The system of claim 1, wherein the object is a first object, and the steps further comprise: determining the location of the second item of the order; determining the distance between the second object and the first object; and The second item is associated with the closest picker in response to the distance being less than a threshold value. The system of claim 6, wherein the threshold value is determined by: calculating a first cost based on separately shipping the first item and the second item; calculating a second shipping cost based on shipping the first item and the second item together; determining the difference between the first cost and the second cost; calculating an amount of time based on the difference; and The threshold value is set to the distance traveled by the closest picker during the amount of time. The system of claim 1, wherein the steps further comprise: determining the number of items that do not have picker associations in the data structure; Determine the average ratio of items per picker; In response to the number exceeding the backlog threshold and the average ratio exceeding the task assignment threshold, reassociating the item with the picker by: Calculate the density of objects in multiple regions; calculating picker densities for the plurality of zones; calculating a participation rate for item density and picker density for each of the plurality of zones; Choose to reassign pickers from the first zone with the lowest participation rate; re-associating the original item from the reassigned picker with the individual picker in the first area in the data structure; in the data structure, re-associating the reassigned items from the pickers in the second region with the highest participation rate; and The location and item identifier associated with the re-associated item is sent to the user device of the reassigned picker. The system of claim 8, wherein the reassociation occurs at regular time intervals. The system of claim 1, wherein the steps further comprise: determining whether the order is normal priority based on the amount of time remaining before the item must be shipped to the customer; and For the pending items in the order, and in response to the order being normal priority, iteratively do the following: identifying the location of the pending object; identifying a plurality of nearby pickers within a threshold distance to the location; For each of the plurality of nearby pickers, the following operations are performed iteratively: determining the location of the previously associated item of the nearby picker; setting the queue distance of the nearby picker to be the distance between the nearby picker and the nearest previously associated item of the nearby picker; Associate the pending item with the nearby picker with the smallest queue distance. A computer-implemented method for object association, comprising: receive instructions for an order that includes at least one item; determining whether the order is urgent based on the amount of time remaining before the item must be shipped to the customer; determining the location of each of the plurality of pickers; For the items in the order, iteratively do the following: Identify the picker closest to the item, the picker having the current work priority; associating the nearest picker with the item in the data structure; in the data structure, re-associating at least one item previously associated with the closest picker with an alternate picker in response to the current work priority not being urgent; sending a location and an item identifier associated with the item to the nearest picker's user device; and After receiving an object completion message from the user device indicating that the object has been picked, a completion flag associated with the object is stored in the data structure. The computer-implemented method of claim 11, wherein The items previously associated with the closest picker are ordered in the data structure according to the respective association time, the first N items of the sequence are fixed and the remaining M items are changeable, wherein the length of the sequence is limited to N+ M objects; and Re-associating at least one item previously associated with the closest picker includes re-associating a transformable item. The computer-implemented method of claim 12, wherein the steps further comprise: receiving a job completion message from a user device of a picker of the plurality of pickers; counting the number of items associated with the picker with the completion flag; comparing the number of objects to a success threshold to determine failure; and When the number of failures exceeds a failure threshold, a signal is sent to lock the user device of the picker. The computer-implemented method of claim 13, wherein the success threshold is based on packaging requirements. The computer-implemented method of claim 12, wherein identifying the picker closest to the item comprises: The second closest picker is selected in response to the first closest picker having a sequence length of N+M. The computer-implemented method of claim 11, wherein the object is a first object, and the steps further comprise: determining the location of the second item of the order; determining the distance between the second object and the first object; and The second item is associated with the closest picker in response to the distance being less than a threshold value. The computer-implemented method of claim 16, wherein the threshold value is determined by: calculating a first cost based on separately shipping the first item and the second item; calculating a second shipping cost based on shipping the first item and the second item together; determining the difference between the first cost and the second cost; calculating an amount of time based on the difference; and The threshold value is set to the distance traveled by the closest picker during the amount of time. The computer-implemented method of claim 11, wherein the steps further comprise: determining the number of items that do not have picker associations in the data structure; Determine the average ratio of items per picker; In response to the number exceeding the backlog threshold and the average ratio exceeding the task assignment threshold, reassociating the item with the picker by: Calculate the density of objects in multiple regions; calculating picker densities for the plurality of zones; calculating a participation rate for item density and picker density for each of the plurality of zones; Choose to reassign pickers from the first zone with the lowest participation rate; re-associating the original item from the reassigned picker with the individual picker in the first area in the data structure; in the data structure, re-associating the reassigned items from the pickers in the second region with the highest participation rate; and The location and item identifier associated with the re-associated item is sent to the user device of the reassigned picker. The computer-implemented method of claim 18, wherein the re-association occurs at regular time intervals. A computer-implemented method for object association, comprising: receive instructions for an order that includes at least one item; prioritizing the order based on the amount of time remaining before the item must be shipped to the customer; determining the location of each of the plurality of pickers; For the pending items of the order, do the following iteratively: accessing at least one data structure to determine the location of the pending object; determining a collection of nearby pickers within a threshold distance to said location; accessing the at least one data structure to determine previous items associated with each of the set of nearby pickers; remove from the set any nearby pickers with a number of previous items above a queue threshold; adding pickers within an extended threshold distance to the set in response to the set being empty after the removal of the pickers; identifying a picker closest to the item from the set of nearby pickers in response to the priority being urgent; Identifying the picker with the smallest queue distance from the set of nearby pickers in response to the priority being normal, where the queue distance represents how far the picker is from the picker's closest associated item ; associating the pending item with the identified picker in the at least one data structure; sending a location and an item identifier associated with the item to the nearest picker's user device; and After receiving a message from the user device that the object has been picked, a completion flag associated with the object is stored in the data structure.

Images